Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks
It addresses the problem of dynamic and flexible 3D operation in 5G multi-MAP networks for network operators, but it appears incremental as it builds on existing DRL and federated learning approaches.
This paper tackles the efficient management of Mobile Access Points (MAPs) in 5G networks by proposing a two-level hierarchical architecture that dynamically reconfigures the network with Integrated Access-Backhaul constraints, resulting in improved generalization and reduced complexity through a federated mechanism for training and sharing placement models.
This paper addresses the efficient management of Mobile Access Points (MAPs), which are Unmanned Aerial Vehicles (UAV), in 5G networks. We propose a two-level hierarchical architecture, which dynamically reconfigures the network while considering Integrated Access-Backhaul (IAB) constraints. The high-layer decision process determines the number of MAPs through consensus, and we develop a joint optimization process to account for co-dependence in network self-management. In the low-layer, MAPs manage their placement using a double-attention based Deep Reinforcement Learning (DRL) model that encourages cooperation without retraining. To improve generalization and reduce complexity, we propose a federated mechanism for training and sharing one placement model for every MAP in the low-layer. Additionally, we jointly optimize the placement and backhaul connectivity of MAPs using a multi-objective reward function, considering the impact of varying MAP placement on wireless backhaul connectivity.